A computerized system composed of neurostimulator and EMG signal acquisition and real-time analysis system has been developed to be used for selective posterior rhizotomy. As a preliminary study for the clinical appli...
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A computerized system composed of neurostimulator and EMG signal acquisition and real-time analysis system has been developed to be used for selective posterior rhizotomy. As a preliminary study for the clinical application of the developed system, we performed a series of animal experiments using cats. From the results of in vivo application, we verified the basic system performance and found that latency, amplitude variance and waveform variance are useful parameters to differentiate sensory nerve from motor nerve. The underlying mechanisms of these phenomena were also provided. These findings were utilized in establishing the objective criterion in nerve selection for selective posterior rhizotomy.
Mathematical modeling of the dynamic behavior of physical systems, gives computers that mimic the capabilities to intelligently monitor and predict their evolutionary characteristics. In this paper, we present a syste...
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Mathematical modeling of the dynamic behavior of physical systems, gives computers that mimic the capabilities to intelligently monitor and predict their evolutionary characteristics. In this paper, we present a system for tracking the time-varying features of non-rigid objects in images of evolving scenes, using the elastic string model of planar contours, which permits the inference and prediction of the quantitative parameters that characterize evolutionary behavior. The goal of our work is to dynamically track non-rigid objects in video sequences, using object alignment techniques based on the properties of the elastic string. We present experimental results of growth cone and neurite tracking in cell growth and motion studies.
Input to the neural network program consists of facial images from a video source. The program uses the back propagation algorithm to train the network and to classify input data based on the subject's posed facia...
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Input to the neural network program consists of facial images from a video source. The program uses the back propagation algorithm to train the network and to classify input data based on the subject's posed facial expression. Training and testing were performed with multiple individuals. The network was trained on a set consisting of 34 happy and 34 sad images from five different subjects. Additionally, the network was tested with images of subjects which were not included in training. In this case, training was performed using 24 happy and 24 sad images of three subjects. Testing was performed using ten happy and ten sad images of two new subjects. In preliminary testing, the network responded correctly for 85% of the 20 test cases. The ability of the network to generalize this discrimination successfully to new individuals is also demonstrated.< >
This paper presents definitions of the terms bioengineering, biomedicalengineering and clinical engineering. These definitions lead to the conclusion that clinical engineers must be individuals with at least a four-y...
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This paper presents definitions of the terms bioengineering, biomedicalengineering and clinical engineering. These definitions lead to the conclusion that clinical engineers must be individuals with at least a four-year bachelor's degree in an engineering specialty who are also well versed in the design, modification and testing of medical instrumentation. Educational programs for clinical engineers in the 1990s must be based upon clear definitions of these professionals' roles. Clinical engineering education should include direct professional experience obtained through internship programs similar to the program described in this paper.
A new laboratory technique has been developed in an effort to simulate the deterioration processes occurring in dental amalgam restorations while they are exposed to attack by the oral environment and simultaneously s...
A new laboratory technique has been developed in an effort to simulate the deterioration processes occurring in dental amalgam restorations while they are exposed to attack by the oral environment and simultaneously subjected to biting forces during mastication. This combined mechanical wear and corrosion action may be one of the major contributors to the degradation of dental amalgam restorations. The technique provides the capability of varying and measuring electrochemical and mechanical parameters during a sliding-wear process in a corrosive environment. A high-copper dental amalgam was selected and tested to demonstrate the applicability of the method in evaluating and studying the effects of the combined action of wear and corrosion processes on dental materials. For the particular amalgam material tested in the present study, it was found that sliding-wear significantly lowered its corrosion potential and increased corrosion rates by at least one order of magnitude.
作者:
Stephen GrossbergSTEPHEN GROSSBERG is a professor of mathematics
psychology and biomedical engineering at Boston University where he is also Director of the Center for Adaptive Systems and of the graduate program in Cognitive and Neural Systems. He is the first president of the International Neural Network Society and a co-editor-in-chief of the Society's journal Neural Networks.
This review outlines the information processing tasks which involve the interaction between the patient and the provider of health care services. These areas include the clinical laboratory, automated multiphasic heal...
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This review outlines the information processing tasks which involve the interaction between the patient and the provider of health care services. These areas include the clinical laboratory, automated multiphasic health testing, medical records, patient monitoring, diagnostic support systems, and medical imaging. Health care professionals, including clinical engineers, must recognize the potential, understand basic principles, and utilize computers effectively during the next decade's rapid advances.
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